C.3. Software Engineering
N. Rezaee; H. Momeni
Volume 8, Issue 2 , Spring 2020, , Pages 189-199
Abstract
Model checking is an automatic technique for software verification through which all reachable states are generated from an initial state to finding errors and desirable patterns. In the model checking approach, the behavior and structure of system should be modeled. Graph transformation system is a ...
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Model checking is an automatic technique for software verification through which all reachable states are generated from an initial state to finding errors and desirable patterns. In the model checking approach, the behavior and structure of system should be modeled. Graph transformation system is a graphical formal modeling language to specify and model the system. However, modeling of large systems with the graph transformation system suffers from the state space explosion problem which usually requires huge amounts of computational resources. In this paper, we propose a hybrid meta-heuristic approach to deal with this searching problem in the graph transformation system because meta-heuristic algorithms are efficient solutions to traverse the graph of large systems. Our approach, using Artificial Bee Colony and Simulated Annealing, replaces a full state space generation, only by producing part of it checking the safety, and finding errors (e.g., deadlock). The experimental results show that our proposed approach is more efficient and accurate compared to other approaches.
C.3. Software Engineering
M. A. Saadtjoo; S. M. Babamir
Volume 6, Issue 2 , Summer 2018, , Pages 375-385
Abstract
Search-based optimization methods have been used for software engineering activities such as software testing. In the field of software testing, search-based test data generation refers to application of meta-heuristic optimization methods to generate test data that cover the code space of a program. ...
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Search-based optimization methods have been used for software engineering activities such as software testing. In the field of software testing, search-based test data generation refers to application of meta-heuristic optimization methods to generate test data that cover the code space of a program. Automatic test data generation that can cover all the paths of software is known as a major challenge. The paper establishes a new cost function for automatic test data generation, which can traverse the non-iterative paths of software control flow graphs. This function is later compared with similar cost functions proposed in other articles. The results indicate the superior performance of the proposed function. Still another innovation in this paper is the application of the Imperialist Competitive Algorithm in automatic test data generation along with the proposed cost function. Automatic test data generation is implemented through the Imperialist Competitive Algorithm as well as the Genetic and Particle Swarm Optimization Algorithms for three software programs with different search space sizes. The algorithms are compared with each other in terms of convergence speed, computational time, and local search. Test data generated by the proposed method has achieved better results than other algorithms in finding the number of non-iterative paths, the convergence speed and computational time with growing the searching space of the software's control flow graph.
C.3. Software Engineering
F. Karimian; S. M. Babamir
Volume 5, Issue 2 , Summer 2017, , Pages 149-167
Abstract
Reliability of software counts on its fault-prone modules. This means that the less software consists of fault-prone units the more we may trust it. Therefore, if we are able to predict the number of fault-prone modules of software, it will be possible to judge the software reliability. In predicting ...
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Reliability of software counts on its fault-prone modules. This means that the less software consists of fault-prone units the more we may trust it. Therefore, if we are able to predict the number of fault-prone modules of software, it will be possible to judge the software reliability. In predicting software fault-prone modules, one of the contributing features is software metric by which one can classify software modules into fault-prone and non-fault-prone ones. To make such a classification, we investigated into 17 classifier methods whose features (attributes) are software metrics (39 metrics) and instances (software modules) of mining are instances of 13 datasets reported by NASA. However, there are two important issues influencing our prediction accuracy when we use data mining methods: (1) selecting the best/most influent features (i.e. software metrics) when there is a wide diversity of them and (2) instance sampling in order to balance the imbalanced instances of mining; we have two imbalanced classes when the classifier biases towards the majority class. Based on the feature selection and instance sampling, we considered 4 scenarios in appraisal of 17 classifier methods to predict software fault-prone modules. To select features, we used Correlation-based Feature Selection (CFS) and to sample instances we did Synthetic Minority Oversampling Technique (SMOTE). Empirical results showed that suitable sampling software modules significantly influences on accuracy of predicting software reliability but metric selection has not considerable effect on the prediction.
C.3. Software Engineering
E. Ghandehari; F. Saadatjoo; M. A. Zare Chahooki
Volume 3, Issue 1 , Winter 2015, , Pages 59-76
Abstract
Agent oriented software engineering (AOSE) is an emerging field in computer science and proposes some systematic ideas for multi agent systems analysis, implementation and maintenance. Despite the various methodologies introduced in the agent-oriented software engineering, the main challenges ...
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Agent oriented software engineering (AOSE) is an emerging field in computer science and proposes some systematic ideas for multi agent systems analysis, implementation and maintenance. Despite the various methodologies introduced in the agent-oriented software engineering, the main challenges are defects in different aspects of methodologies. According to the defects resulted from weaknesses in agent oriented methodologies in different aspects, a combinatory solution named ARA using, ASPECS, ROADMAP and AOR has been proposed. The three methodologies were analyzed in a comprehensive analytical framework according to concepts and Perceptions, modeling language, process and pragmatism. According to time and resource limitations, sample methodologies for evaluation and in titration were selected. This selection was based on the use of methodologies' and their combination ability. The evaluation show that, the ROADMAP methodology supports stages of agent-oriented systems' analysis and the design stage is not complete because it doesn’t model all semi agents. On the other hand, since AOR and ASPECS methodologies support the design stage and inter agent interactions, a mixed methodology has been proposed and is a combination of analysis stage of ROADMAP methodology and design stage of AOR and ASPECS methodologies. Furthermore, to increase the performance of proposed methodology of actor models, service model, capability and programming were also added to this proposed methodology. To describe its difference phases, it was used in a case study too. Results of this project can pave the way to introduce future agent-oriented methodologies.